Being that this is “practical” AI, we decided that it would be good to take time to discuss various aspects of AI infrastructure. In this full-connected episode, we discuss our personal/local infrastructure along with trends in AI, including infra for training, serving, and data management.
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Featuring:
- Chris Benson – Website, GitHub, LinkedIn, X
- Daniel Whitenack – Website, GitHub, X
Show Notes:
Our locally installed stuff:
- Jupyter
- Docker
- Python
- Go
- Postman
Where we see AI workflows running:
- AWS
- GCP
- Azure
- Kubernetes and KubeFlow
- On-prem workstations:
- NVIDIA
- Lambda Labs
- System76
Experimentation / model development:
- JupyterLab
- Google Colaboratory
- AWS SageMaker
- Data Science platforms:
- Domino
- DataBricks
- DataRobot
- H2O.ai
Pipelining and automation:
- Pachyderm
- Airflow
- Luigi
- Model optimization:
- OpenVino
- TensorRT
- TensorFlow Lite
Serving:
- MXNet
- TensorFlow serving
- Seldon
Monitoring/visibility:
- TensorBoard
- Netron
- Knock knock
- Prometheus
- ElasticSearch
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